Bar Graph2
David Asare Kumi
4/2/2020
Basic Bar Graphs
library(gcookbook)
library(ggplot2)
library(plotly)
data(pg_mean)
g<-ggplot(pg_mean,aes(x=group,y=weight))+geom_bar(stat="identity",fill="yellow",color="black")+
ggtitle("Barplot of Group and Weight")
ggplotly(g)Grouping Bars Together
- Group bars together by a second variable.
library(gcookbook)
library(ggplot2)
library(plotly)
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",position="dodge") + xlab("Date")+ ylab("Weight")+ggtitle("Group Barplot of Date and Weight")
ggplotly(g)It is important to note that, if you don’t use position=“dodge”, you will end up with a stacked bar graph.
Variables mapped to the fill color must be categorical or factor variables and not continuous variables.
Set the colors
Use scale_fill_brewer()
Use scale_fill_manual()
library(gcookbook)
library(ggplot2)
library(plotly)
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",position="dodge",
color="black")+scale_fill_brewer(palette="Dark2") + xlab("Date")+ ylab("Weight")+ggtitle("Group Barplot of Date and Weight")
ggplotly(g)If there are missing observations in the categorical variable, the bar will be missing and the neighbouring bars will expand to fill that space.
If your data has this issue, you can manually make an entry for the missing factor level combination with an NA for the y variable.
Making a bar graph of counts
library(datasets)
library(ggplot2)
library(plotly)
data(diamonds)
g<-ggplot(diamonds,aes(x=cut,fill=cut))+ geom_bar()+xlab("Cut")+ylab("Count")+
ggtitle("Barplot showing count/frequency of cut ")
ggplotly(g)In this example, with geom_bar(), the default behaviour is to use stat=“bin” which counts up the number of cases for each group.
If we use a continuous variable on the x-axis, we get a histogram.
Continuous variable resulting in a histogram instead of a bar graph
library(datasets)
library(ggplot2)
library(plotly)
data(diamonds)
g<-ggplot(diamonds,aes(x=carat))+ geom_bar(stat="bin")+xlab("Carat")+ylab("Count")+
ggtitle("Barplot showing count/frequency of Carat ")
ggplotly(g)Using colors in a bar graph
library(gcookbook)
library(ggplot2)
library(plotly)
data(uspopchange)
upc<-subset(uspopchange,rank(Change)>40)
g<-ggplot(upc,aes(x=Abb,y=Change,fill=Region))+geom_bar(stat="identity")
ggplotly(g)Set colors manually
library(gcookbook)
library(ggplot2)
library(plotly)
data(uspopchange)
upc<-subset(uspopchange,rank(Change)>40)
g<-ggplot(upc,aes(x=reorder(Abb,Change),y=Change,fill=Region))+geom_bar(stat="identity",
color="black")+scale_fill_manual(values=c("blue","yellow"))+xlab("State")
ggplotly(g)# you can set the color using scale_fill_manual(values=c("#669933","#FFCC66"))Coloring Negative and Positive bars differently
library(gcookbook)
library(ggplot2)
library(plotly)
data(climate)
csub<-subset(climate,Source=="Berkeley" & Year>=1900)
csub$pos<-csub$Anomaly10y>=0
g<-ggplot(csub,aes(x=Year,y=Anomaly10y,fill=pos))+geom_bar(stat="identity",position="identity")+
scale_fill_manual(values=c("yellow","blue"))+ggtitle("Barplot of Year and Anomaly10y")
ggplotly(g)We can change the colors with scale_fill_manual() or scale_fill_brewer()
Remove the legend with guide=FALSE - this is deprecated.
During the plot, you can turn off legends by using the argument show.legend=FALSE in the geom_bar().
After the plot creation, it’s possible to remove the legend using the code p + theme(legend.position=“none”).
Add a thin black line around each of the bars by setting color=“black” and specify size which is the thickness of the outline in millimetres.
library(gcookbook)
library(ggplot2)
library(plotly)
data(climate)
csub<-subset(climate,Source=="Berkeley" & Year>=1900)
csub$pos<-csub$Anomaly10y>=0
g<-ggplot(csub,aes(x=Year,y=Anomaly10y,fill=pos))+geom_bar(stat="identity",position="identity",
color="black",size=0.25)+scale_fill_manual(values=c("#CCEEFF","#FFDDDD"))+ggtitle("Barplot of Year and Anomaly10y")+theme(legend.position="none")
ggplotly(g)Adjusting Bar Width and Spacing
Set width in geom_bar(). The default value is 0.9. Larger values make the bars wider and smaller values make the bars narrower.
The maximum width is 1.
#Standard width
library(gcookbook)
library(ggplot2)
library(plotly)
library(gridExtra)
data(pg_mean)
g1<-ggplot(pg_mean,aes(x=group,y=weight,fill=group))+geom_bar(stat="identity")+xlab("Group")+
ylab("Weight")+ggtitle("Barplot of Group and Weight")
ggplotly(g1)#For narrow bars
g2<-ggplot(pg_mean,aes(x=group,y=weight,fill=group))+geom_bar(stat="identity",width=0.5)+xlab("Group")+ ylab("Weight")+ggtitle("Barplot of Group and Weight")
ggplotly(g2)#For wider bars
g3<-ggplot(pg_mean,aes(x=group,y=weight,fill=group))+geom_bar(stat="identity",width=1)+xlab("Group")+ ylab("Weight")+ggtitle("Barplot of Group and Weight")
ggplotly(g3)grid.arrange(g1,g2,g3,ncol=3)
Create Space in grouped bars and some space between the bars
library(gcookbook)
library(ggplot2)
library(plotly)
library(gridExtra)
data(cabbage_exp)
#space between grouped bars
g1<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",width=0.5,
position="dodge")+ggtitle("Barplot of Date and Weight")
ggplotly(g1)#some space between the bars
g2<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",width=0.5,
position=position_dodge(0.7))+ggtitle("Barplot of Date and Weight")
ggplotly(g2)#apply gridExtra package
grid.arrange(g1,g2,ncol=2)
position=“dodge” has a default value of 0.9. If we want to set a specific value then we use position=position_dodge() for the bars.
The default width value for position _dodge() is the same as width in geom_bar().
Making a stacked bar graph plus the label in the middle of the stack
library(gcookbook)
library(ggplot2)
library(plotly)
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity")+xlab("Date")+
ylab("Weight")+ggtitle("Stacked bar graph of Date and Weight")+geom_text(aes(label=Weight),
size=3,position=position_stack(vjust=0.5))
ggplotly(g)- Note that x is a categorical or factor variable and fill is also mapped onto another categorical or factor variable.
Reverse the order of the legend
library(gcookbook)
library(ggplot2)
library(plotly)
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity")+
guides(fill=guide_legend(reverse=TRUE))+ xlab("Date")+ylab("Weight")+ggtitle("Stacked bar graph of Date and Weight")
ggplotly(g)- The structure of the cabbage_exp data.
There are three levels of Date.
There are two levels of Cultivar.
To reverse the stacking order
- Specify order = desc() in the aesthetic mapping.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar,order=desc(Cultivar)))+
geom_bar(stat="identity")+xlab("Date")+ylab("Weight")+ggtitle("Stacked bar graph of Date and Weight")
ggplotly(g)For a more polished graph
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
data(cabbage_exp)
g<-ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",color="black")+ guides(fill=guide_legend(reverse=TRUE))+scale_fill_brewer(palette="Pastel1")+xlab("Date")+ylab("Weight")+ggtitle("Stacked bar graph of Date and Weight")
ggplotly(g)Making a proportional stacked bar graph (%age)
- Scale the data to 100% within each stack.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
data(cabbage_exp)
#Do a group wise transform splitting on "Date".
ce<-ddply(cabbage_exp,"Date",transform,percent_weight=Weight/sum(Weight)*100)
g<-ggplot(ce,aes(x=Date,y=percent_weight,fill=Cultivar))+geom_bar(stat="identity")
ggplotly(g)Adding labels to a bar graph
- Add geom_text to your graph and set vjust i.e. vertical justification which enables you to move the text above or below the tops of the bars.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
library(gridExtra)
data(cabbage_exp)
#below the top
g1<-ggplot(cabbage_exp,aes(x=interaction(Date,Cultivar),y=Weight))+geom_bar(stat="identity")+
geom_text(aes(label=Weight),vjust=1.5,color="white")
#above the top
g2<-ggplot(cabbage_exp,aes(x=interaction(Date,Cultivar),y=Weight))+geom_bar(stat="identity")+
geom_text(aes(label=Weight),vjust=-0.2)
grid.arrange(g1,g2,ncol=2)
- Consider your ylimt when you want to put the text on top of the bars. Y upper limit must be a bit higher.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
library(gridExtra)
data(cabbage_exp)
#Adjjust y limits to be a little higher
g1<-ggplot(cabbage_exp,aes(x=interaction(Date,Cultivar),y=Weight))+geom_bar(stat="identity")+
geom_text(aes(label=Weight),vjust=-0.2)+ylim(0,max(cabbage_exp$Weight)*1.05)
#map y position slightly above the top of the bar
g2<-ggplot(cabbage_exp,aes(x=interaction(Date,Cultivar),y=Weight))+geom_bar(stat="identity")+
geom_text(aes(y=Weight+0.1,label=Weight))
grid.arrange(g1,g2,ncol=2)
For grouped bar graph
Use position =position_dodge() and give it a value for the dodging width- default is 0.9.
Specify size - default is 5.
make it smaller by using 3.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
library(gridExtra)
data(cabbage_exp)
ggplot(cabbage_exp,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",position="dodge")+
geom_text(aes(label=Weight),vjust=1.5,color="white",position=position_dodge(0.9),size=4)
Putting labels on stacked bars
- Find the cumulative sum for each stack.
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
library(gridExtra)
data(cabbage_exp)
#sort the data
ce<-arrange(cabbage_exp,Date,Cultivar)
#get the cumulative sum
ce<-ddply(ce,"Date",transform,label_y=cumsum(Weight))
ce Cultivar Date Weight sd n se label_y
1 c39 d16 3.18 0.9566144 10 0.30250803 3.18
2 c52 d16 2.26 0.4452215 10 0.14079141 5.44
3 c39 d20 2.80 0.2788867 10 0.08819171 2.80
4 c52 d20 3.11 0.7908505 10 0.25008887 5.91
5 c39 d21 2.74 0.9834181 10 0.31098410 2.74
6 c52 d21 1.47 0.2110819 10 0.06674995 4.21g1<-ggplot(ce,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity")+geom_text(aes(y=label_y,
label=Weight),vjust=1.5,color="white")
#placing the label in the middle of each stacked bar
ce<-arrange(cabbage_exp,Date,Cultivar)
#calculate y position placing it in the middle
ce<-ddply(ce,"Date",transform,label_y=cumsum(Weight)-0.5*Weight)
g2<-ggplot(ce,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity")+
geom_text(aes(y=label_y,label=Weight),color="white")
grid.arrange(g1,g2,ncol=2)
For a more polished graph
library(gcookbook)
library(ggplot2)
library(plotly)
library(plyr) # needed for desc()
library(gridExtra)
data(cabbage_exp)
#placing the label in the middle of each stacked bar
ce<-arrange(cabbage_exp,Date,Cultivar)
#calculate y position placing it in the middle
ce<-ddply(ce,"Date",transform,label_y=cumsum(Weight)-0.5*Weight)
ggplot(ce,aes(x=Date,y=Weight,fill=Cultivar))+geom_bar(stat="identity",color="black")+
geom_text(aes(y=label_y,label=paste(format(Weight,nsmall=2),"kg")),size=4)+
guides(fill=guide_legend(reverse=TRUE))+scale_fill_brewer(palette="Pastel1")
Add data label to simple bargraph
library(datasets)
library(ggplot2)
library(plotly)
data(diamonds)
g<-ggplot(diamonds,aes(x=cut,fill=cut))+ geom_bar()+xlab("Cut")+ylab("Count")+
ggtitle("Barplot showing count/frequency of cut ")+geom_text(stat="count",aes(label=..count..),
vjust=-0.2)
ggplotly(g)